Companies from convenience stores to retail giants are discovering that pocket-sized artificial intelligence (AI) can deliver big savings as agile new language models match their behemoth predecessors at a fraction of the cost.
H2O.ai recently launched small models as just one example of a broader shift toward compact AI models that promise to change the way companies handle everything from inventory management to customer service. These streamlined systems can process purchase orders, receipts, and store data with the same accuracy as massive language models, while reducing computing costs.
“Smaller AI models can level the playing field between large retailers and small/medium businesses (SMBs) by providing cost-effective solutions for inventory management and customer service automation,” said Steven Sermarini, senior director of Engineering-Data & Analytics at eCommerce Logistics and payments company Radial, told PYMNTS. “These models enable SMBs to optimize inventory levels, predict demand and automate ordering while improving operational efficiency.”
A small language model is a streamlined AI system that processes text using millions of parameters instead of billions. It trades some advanced capabilities for faster performance and lighter computing requirements.
Small is the new big in AI
H2O.ai has released two smaller AI models (0.8 billion and 2 billion parameters) for document processing and text recognition. The models are available for free on Hugging Face, trained on millions of conversation pairs. The company claims that the 0.8B model outperforms larger competitors in optical character recognition (OCR) benchmarks.
“We designed H2OVL Mississippi Models to be a powerful yet cost-effective solution, bringing AI-powered OCR, visual understanding and document AI to enterprises,” said Sri Ambati, CEO and founder of H2O.ai, in a press release. “By combining state-of-the-art multimodal AI with extreme efficiency, H2OVL Mississippi delivers accurate, scalable AI solutions for documents across a range of industries.”
Sermarini pointed out that smaller models like H20’s are typically quicker to prototype and develop.
“This will allow new, creative players to quickly design advanced analytics solutions for retail, supply chain and logistics,” he says.
While smaller, more efficient models reduce computing barriers, deploying AI in the logistics and retail industries means more than just cost savings, Stephen DeAngelis, founder and CEO of AI company Enterra Solutions, told PYMNTS.
“Language models still require extensive training, high-quality data and domain expertise to address complex operational challenges in supply chain management and business analytics,” he added. “The technology transition is likely to be gradual rather than immediate.”
The emergence of efficient, smaller AI models opens doors for startups that cannot afford huge computing costs. These lightweight models allow new companies to compete and innovate without requiring huge budgets for AI infrastructure, Hardik Chawla, senior product manager of technical at Amazon, told PYMNTS. He said he sees many companies building robust retail solutions that run on modest hardware.
“Rather than needing massive GPU clusters, startups can deploy targeted models that perform specific high-value tasks – from dynamic pricing to demand forecasting,” he added.
“Think of a startup that focuses on predicting inventory shortages. Instead of building a comprehensive AI system for retail, they can deploy a small, efficient model specifically trained on inventory patterns. This targeted approach not only reduces development and operating costs, but often produces better results than trying to extract this functionality from a larger, generic model.”
Helping and not replacing people
Despite the benefits of smaller AI models, observers don’t see AI replacing human workers anytime soon. Chawla said that in retail, warehousing and customer service, many routine, manual tasks can be automated, potentially reducing time spent on repetitive work.
For example, in customer service, Chawla said an AI can handle common questions such as order status, while an agent steps in for more personalized support. This enhanced approach improves efficiency and improves service quality, making personalized, AI-enabled customer interactions scalable for businesses of all sizes.
Likewise, these warehouse operations models can help employees by optimizing workflows, suggesting the most efficient routes or flagging potential inventory issues in real time. Employees still perform the physical tasks, but AI supports them by reducing errors and increasing productivity.
“Regardless of the sector, data literacy, AI monitoring and technology-driven process management will become highly sought-after skills,” Chawla said. “To prevent displacement, we need a major push for reskilling and upskilling initiatives. It’s about shifting the workforce into new types of roles, rather than eliminating jobs completely.”
